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Record W2335563122 · doi:10.1109/tsg.2016.2533164

Estimating Power Generation of Invisible Solar Sites Using Publicly Available Data

2016· article· en· W2335563122 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Smart Grid · 2016
Typearticle
Languageen
FieldComputer Science
TopicSolar Radiation and Photovoltaics
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPhotovoltaic systemSolar powerElectricity generationElectric power systemPower (physics)Computer scienceScale (ratio)EngineeringReliability engineeringElectrical engineeringGeography

Abstract

fetched live from OpenAlex

Large-scale integration of invisible solar photovoltaic generation into power systems could significantly affect the system net load and pose new challenges in the operation of power systems. Invisible solar photovoltaic refers mainly to small-scale roof-top solar sites that are not monitored, and thus are invisible to utilities and system operators. Invisible solar generation affects the shape of system net electrical load and could make net load forecasting more challenging. In this paper, a methodology is proposed to estimate the power generation of invisible solar photovoltaic sites. The proposed method only uses the measured power generation data of publicly available sites. It uses real time data of a small subset of sites to estimate the aggregated power generation from known sites within a region. The proposed model is validated using actual invisible solar generation data of the California power system.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.777
Threshold uncertainty score0.416

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.123
GPT teacher head0.290
Teacher spread0.167 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it